CN1272003A - Information channel estimation and compensation method based on channel estimation - Google Patents
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Abstract
The method of channel estimation determines linear regression coefficients for N blocks of a first signal component in a received signal on a block-by-block basis, and determines a channel estimate based on the linear regression coefficients for the N blocks. Using the channel estimate, channel distortion in a second signal component of the received signal can be significantly eliminated.
Description
The present invention relates to the field of wireless communication, and more particularly, to a method for channel estimation and compensation based on the channel estimation.
In recent decades, many new techniques such as multi-carrier transmission and inventive antenna algorithms have been proposed to increase the capacity of multi-user wireless communication systems. However, the performance improvements afforded by such new techniques are often limited by poor accuracy of channel estimation. Channel estimation is the estimation of the distortion between the transmitter and receiver signals introduced by the physical channel or medium through which the signals are transmitted. By using an estimate of the distortion (i.e., a channel estimate), a portion of the distortion may be removed in the receiver, improving the accuracy of the received signal. Even small improvements in channel estimation can provide significant benefits to, for example, multi-user techniques.
Conventional channel estimation techniques generally consider the physical channel and the characteristics of the distortion caused by it to be time-invariant over a sampling window of finite length. The channel estimate is determined by averaging some reference number observed over a sampling window and then applying the average to remove distortion in the received signal. Examples of such conventional techniques include Recursive Least Squares (RLS), Least Mean Squares (LMS), and running mean filtering, among others.
However, in wireless mobile communication systems including mobile stations, for example, these techniques will not be able to adapt and produce the desired results in rapidly changing environments; here, the physical channel parameters may change rapidly. Moreover, in Code Division Multiple Access (CDMA) systems, a high signal-to-noise ratio (SNR) is required to maximize channel capacity, and strong noise further complicates the channel estimation problem because the channel estimation technique must meet a pair of conflicting requirements, namely a small window size for time invariance and a large window size for noise immunity.
In the channel estimation method according to the invention it is assumed that the channel parameters are linearly varying over time. By performing a linear regression operation on samples within a sampling window of a first signal component in the received signal, a channel estimate that varies with rapidly changing environments may be obtained.
By determining linear regression coefficients for each block of samples, the computational complexity of linear regression can be simplified. New samples of the received signal are obtained on a block-by-block basis and new regression coefficients are derived for this. Applying both the linear regression coefficients determined for the new block of samples and the linear regression coefficients stored for the previous blocks of samples, linear regression coefficients may be determined for the entire window.
By adding the complex conjugate of the channel estimate to the second signal component in the received signal, channel distortion in the second signal component can be greatly reduced.
Furthermore, by estimating the distortion caused by the frequency offset (frequency mismatch between the transmitter modulator and the receiver demodulator) and compensating for the frequency offset before performing channel estimation, a much larger sampling window can be applied to perform channel estimation. In this way, greater noise immunity will be transferred.
The present invention will become more fully understood from the detailed description given herein below and the accompanying drawings given by way of example; in the description, like reference numerals designate corresponding parts throughout the several views; wherein,
FIG. 1 illustrates an apparatus for generating channel estimates and implementing channel estimation-based compensation in accordance with the present invention; and
fig. 2 shows a flow chart of a method according to the invention for generating a channel estimate.
Fig. 1 shows an arrangement according to the invention for generating channel estimates and for implementing compensation based on the channel estimates. As shown in the figure, the received signal is applied to a frequency offset estimator 10. Using a known signal component in the received signal, the frequency offset estimator 10 estimates the frequency offset of the received signal.
The following equation (1) generally represents a received symbol of one signal component in a received signal.
Where r (t) is the received symbol, s (t) is the transmitted symbol, f (t) is the frequency offset, and g (t) is the channel distortion (1).
Since the oscillator used in the receiver to demodulate the signal is not perfectly synchronized with the oscillator used in the transmitter to modulate the signal, there will be an oscillating bias between them, presenting a distortion in the received symbols called frequency offset. In contrast to channel distortion, this distortion appears as a periodic signal over a small sampling window. The frequency offset estimator 10 generates a frequency offset estimateEstimating the frequency offset
And the received symbols r (t) are applied to a frequency offset compensator 12.
The frequency offset compensator 12 applies the estimated frequency offset
To compensate for frequency offsets in the received signal and to produce a compensated received signal r' (t) in the known and unknown signal components of the received signal.
The compensated received symbols r '(t) are received by the channel estimator 14, and the compensated received symbols r' (t) in the known signal component and a window size are applied to produce an estimate of the channel distortion g (t)
Channel estimation, as explained in detail belowLinear regression coefficients will be constructed. The channel estimator 14 provides the channel estimate to the channel estimate compensator 16And compensated received symbols r' (t). Channel estimation compensator 16 applies channel estimation
To compensate for channel distortion in the compensated received symbol r' (t) after the unknown signal component in the received signal.
Preferably, but not necessarily, the apparatus further comprises a window size generator 18 for adaptively generating a window size W based on linear regression coefficients from the channel estimator 14.
Now, the operation of the present invention will be described in detail. As described above, it is applied to compensate for known and unknown components in the received signal based on an estimate of the symbols in the known signal component of the received signal. Some wireless communication systems, such as CDMA 2000, include known signal components in uplink (received by a base station) and downlink (transmitted by a base station) communications. In CDMA 2000, pilot signals in uplink and downlink communications are known signal components.
Alternatively, a known symbol sequence, called a training sequence, is periodically inserted into an unknown signal component, which can be used as the known signal component. Such periodic training sequences are included, for example, in Time Division Multiple Access (TDMA) systems.
As yet another method, non-coherent detection is performed on the unknown signal components. Non-coherent detection is the estimation of data symbols without taking into account channel distortion. The result of the non-coherent detection is then treated as the known signal component.
As previously mentioned, the frequency offset appears as a periodic signal, which can be represented by the following equation:
where w is the angular frequency difference between the local oscillator of the demodulator in the receiver and the oscillator of the modulator in the transmitter (jwt) (2). Therefore, w can be estimated by averaging the phase difference between adjacent symbols over time, as shown in equation (3) below: <math> <mrow> <mover> <mi>w</mi> <mo>^</mo> </mover> <mo>=</mo> <mo>[</mo> <munderover> <mi>Σ</mi> <mrow> <mi>n</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>s</mi> </munderover> <mi>phase</mi> <msup> <mrow> <mo>(</mo> <mi>a</mi> <mrow> <mo>(</mo> <mi>nΔt</mi> <mo>)</mo> </mrow> <mo>·</mo> <mi>a</mi> <mrow> <mo>(</mo> <mi>nΔt</mi> <mo>-</mo> <mi>Δt</mi> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>*</mo> </msup> <mo>/</mo> <mi>Δt</mi> <mo>]</mo> <mo>/</mo> <mi>s</mi> </mrow> </math> where a (n Δ t) is the known signal component in the received signal at time n Δ t, a (n Δ t- Δ t) is the known signal component in the received signal at time (n-1) Δ t, Δ t is the time interval between adjacent symbols, and s is the number of samples used to generate the estimateTo achieve the purpose.
The frequency offset estimator 10 estimates w according to equation (3) and applies the frequency offset in equation (2)
Generating a frequency offset estimate
The frequency offset compensator 12 estimates the frequency offset byIs added to the received symbol r (t) of the received signal to compensate the frequency offset f (t), resulting in a compensated received symbol r' (t) of the received signal. By compensating by estimating the frequency offset and comparing, a larger sampling window can be applied to perform channel estimation, with the attendant advantage of greater noise immunity.
The channel estimator 14 receives samples of the known signal on a block-by-block basis and determines a channel estimate for the number N of blocks of samples corresponding to the window size W. In installing a wireless communication system employing the present invention, the range of possible window sizes is determined by applying any known technique based on the expected range of doppler shifts, which is determined by the speed at which the mobile station is likely to move. After the maximum and minimum window sizes are obtained, the maximum and minimum number of sample blocks in each window can be determined according to the formula N ═ w/2 ^ (1/2). An integer value between the maximum and minimum number of sample blocks may be selected as the number of sample blocks in each window, according to designer preference. Preferably, but not necessarily, the number of blocks of samples is chosen such that an integer multiple thereof is equal to the size of a data frame specified in the radio standard according to which the radio communication system is operating. Thus, in addition to setting the number of sample blocks, N, the present processing method also establishes an initial window size, W, and a sample block size.
The operation of the channel estimator 14 to generate a channel estimate will now be described with respect to the flow chart shown in figure 2.
In step S20, the count value C is set to 1, and in step S25, it is determined by the channel estimator 14 whether the value of C is greater than N-1. If the value of C is less than N-1, then the samples in the next sample block, i.e., the t-th sample block, of the known signal component are received and stored in step S30. The storage of these samples replaces the storage of the previous block of samples so that the channel estimator 14 stores only one block of samples at a time. It should be understood that the channel estimator 14 is not limited to storing only one block of samples, but that this reduces the memory requirements of the channel estimator 14.
In step S35, the channel estimator 14 calculates and stores a linear regression coefficient for the C-th sample block. In particular, the channel estimator 14 calculates the mean and slope of a curve that minimizes the mean variance of the samples in the C-th block. Then, in step S40, a channel estimate is calculated by the channel estimator 14 using the linear regression coefficients stored for the sample blocks 1 to C. In particular, the channel estimator 14 calculates the mean and slope of a curve that minimizes the mean variance of the samples in blocks 1 through C, as represented by the linear regression coefficients for each block of samples. It is not necessary to apply its samples for each block of samples, instead only linear regression coefficients need be applied and it is not necessary to store their samples for the 1 to C blocks.
After step S40, the count value C is incremented by 1 in step S50, and the process returns to step S25. After processing the block of N-1 samples and storing the linear regression coefficients for this, the count C will exceed N-1. Then, the process proceeds from step S25 to step S25, and samples over the entire sampling window are received as the next block of samples is received.
In step S55, the next sample value block of the known signal component is received and stored, i.e., the C-th sample value block is received and stored. These samples are stored instead of the previously stored blocks of samples so that the channel estimator 14 still stores only one block of samples at a time. As mentioned above, the channel estimator 14 is not limited to storing only one block of samples, but this reduces the memory requirements of the channel estimator 14.
Then, in step S60, the channel estimator 14 calculates the mean and slope of a curve that minimizes the mean variance of the samples in the C-th block. Next, in step S65, the channel estimator 14 calculates a channel estimate by applying the linear regression coefficients stored for the sample blocks (C +1-N) to C (the current total N blocks or the size of the sample window). Specifically, the channel estimator 14 calculates the mean and slope of a curve that minimizes the mean variance of the samples in blocks (C +1-N) through C, as represented by the linear regression coefficients for each block of samples. The samples of each block of samples are not applied here, but instead only linear regression coefficients are applied. Thus, only linear regression coefficients are stored for the block of N-1 samples, not the samples. This greatly reduces the memory requirements on the channel estimator 14.
Next, in step S70, the linear regression coefficient of the C-th sample block is stored instead of the linear regression coefficient of the (C +1-N) -th sample block. After step S70, the count value C is incremented by 1 in step S50, and the process returns to step S25.
Unlike conventional channel estimation techniques, the channel estimation method according to the present invention is not considered to be a constant channel within the observation window. Conversely, the channel is considered to be linearly varying with time (i.e., an arbitrary straight line in the complex plane).
Furthermore, channel estimates are generated by linearly regressing the updated sample blocks of coefficients and only updating one sample block at a time to shift the observation window or sampling window, so that the processing delay and computational complexity caused by a large observation window is greatly reduced compared to applying the samples within the observation window itself. It should be understood, however, that instead of applying linear regression coefficients, the samples themselves can be used to generate the channel estimate. Of course, such an implementation would require a significantly larger amount of memory.
For example, in a CDMA 2000 wireless system, the unknown signal component may be a traffic channel. In the above discussion of the training sequence, in another approach the unknown signal component may be a non-training sequence portion of the received signal. In yet another method for noncoherent detection of a received signal, a received signal before noncoherent detection and a received signal after noncoherent detection in an unknown signal component are the unknown signal component.
Next, the operation of the window size generator 18 will be described. In the preferred embodiment, the sampling window size W is determined by the window size generator 18 based on linear regression coefficients from the channel estimator 14. However, the window size generator 18 provides the predetermined window size discussed to the channel estimator 14 before generating the linear regression coefficients. The sampling window (also called observation window) size is a design parameter that can be determined empirically to best meet the designer's intent; and thus fixes it. However, as described above, in the preferred embodiment, the sampling window size W is adaptively determined according to the following equation (4) based on the linear regression coefficient from the channel estimator 14.
W ═ alpha1 ^ PI (2 ^ beta/delta) ^1/2+ alpha2 (4) where alpha1 and alpha2 are empirically determined constants that best meet the designer's wishes; in addition, the air conditioner is provided with a fan, <math> <mrow> <mi>beta</mi> <mo>=</mo> <mfrac> <mn>1</mn> <mi>L</mi> </mfrac> <munderover> <mi>Σ</mi> <mrow> <mi>i</mi> <mo>=</mo> <mi>C</mi> <mo>-</mo> <mi>L</mi> </mrow> <mrow> <mi>C</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mrow> <mo>|</mo> <msub> <mi>k</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mrow> </math> <math> <mrow> <mi>delta</mi> <mo>=</mo> <mfrac> <mrow> <munderover> <mi>Σ</mi> <mrow> <mi>i</mi> <mo>=</mo> <mi>C</mi> <mo>-</mo> <mi>L</mi> </mrow> <mrow> <mi>C</mi> <mo>-</mo> <mn>1</mn> </mrow> </munderover> <msup> <mrow> <mo>[</mo> <mrow> <mo>(</mo> <msup> <mi>n</mi> <mn>2</mn> </msup> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>)</mo> </mrow> <mo>-</mo> <mn>2</mn> <mi>n</mi> <msub> <mi>k</mi> <mn>0</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mo>]</mo> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mn>2</mn> <mrow> <mo>(</mo> <msup> <mi>n</mi> <mn>2</mn> </msup> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msup> <mrow> <mn>4</mn> <mi>n</mi> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <msup> <mrow> <mo>|</mo> <msub> <mi>k</mi> <mn>1</mn> </msub> <mrow> <mo>(</mo> <mi>i</mi> <mo>)</mo> </mrow> <mo>|</mo> </mrow> <mn>2</mn> </msup> </mrow> <mrow> <mi>L</mi> <mo>-</mo> <mrow> <mo>(</mo> <msup> <mrow> <mn>7</mn> <mi>n</mi> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mrow> <mo>(</mo> <msup> <mrow> <mn>4</mn> <mi>n</mi> </mrow> <mn>2</mn> </msup> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow> </mfrac> <mo>;</mo> </mrow> </math> here, k0(i) Is the average value of the ith sample value block; k is a radical of1(i) Is the slope of the ith sample block; n is the size of the sample block; l is the observation period over which the doppler shift is assumed to be constant (a set of design parameters according to the designer's wishes); and C is the sample block index or count value discussed above.
If the window size determined according to equation (4) is one sample block size larger than the current window size or one sample block size smaller than the current window sizeThe size of the sample block, the window size generator 18 increases or decreases the window size by one sample block, respectively. Thus, the number N of sample blocks increases or decreases, respectively. The new number of sample blocks N (i.e., the window size) is applied to the channel estimator 14 to generate a channel estimate
It will be appreciated that the method of determining the window size is not limited to the method discussed above, and that any method may be applied.
It will be appreciated that the method according to the invention may be implemented by a suitably programmed digital signal processor or ASIC having sufficient memory capacity and that the digital signal processor or ASIC resides in a receiver which receives the transmitted signal. The method according to the invention is therefore applicable to both mobile stations and base stations in a wireless communication system.
Claims (19)
1. A method of channel estimation, comprising the steps of:
a) determining linear regression coefficients for N blocks of samples within a first signal component of a received signal, each of said N blocks of samples comprising a plurality of samples of said first signal component; and
b) and obtaining a channel estimate according to the linear regression coefficients of the N sample blocks.
2. The method of claim 1, further comprising, prior to said step a), the steps of:
c) compensating for a frequency offset in the first signal component.
3. The method of claim 1, wherein said step a) comprises the steps of:
a1) storing a linear regression coefficient for each of 1 st to (N-1) th sample blocks of the N sample blocks;
a2) receiving samples in an nth sample block of said N sample blocks; and
a3) linear regression coefficients are determined for the nth block of samples using the received samples.
4. The method of claim 3, wherein said step a1) stores a slope and an average of a curve for each of said 1 st through (N-1) th sample blocks, as said linear regression coefficients for said 1 st through (N-1) th sample blocks, which minimizes the mean variance of samples within a corresponding one of said 1 st through (N-1) th sample blocks.
5. The method of claim 4, wherein said step a3) determines a slope and a mean of a curve, as said linear regression coefficients for said nth block of samples, that minimizes the mean variance of said received samples in said nth block of samples.
6. The method of claim 5, wherein said step b) obtains a slope and a mean of a curve that minimizes a mean variance of samples in said 1 st to nth blocks as represented by said linear regression coefficients of said 1 st to nth blocks as said channel estimate.
7. The method of claim 3, wherein said step a3) determines a slope and a mean of a curve, as said linear regression coefficients for said nth block of samples, that minimizes the mean variance of said received samples in said nth block of samples.
8. The method of claim 3, wherein a slope and a mean of a curve that minimizes a mean variance of samples in said 1 st to nth blocks of samples, as represented by said linear regression coefficients of said 1 st to nth blocks of samples in said channel estimate, are obtained as said channel estimate in said step b).
9. The method of claim 3, wherein said step a) stores samples only for said nth block of samples.
10. The method of claim 1, wherein said first signal component is a known signal component.
11. The method of claim 10, wherein said known signal component is a pilot signal in a CDMA 2000 system.
12. The method of claim 10, wherein the known signal component is a periodic training sequence.
13. The method of claim 1, wherein said first signal component is non-coherent detection of a data signal.
14. The method of claim 1, further comprising the step of:
c) the symbols of the second signal component are determined using the channel estimate.
15. The method of claim 4, wherein said first signal component is a periodic training sequence in a signal segment.
16. The method of claim 15, wherein said second signal component is a non-training sequence portion of said signal segment.
17. The method of claim 14, wherein said first signal component is non-coherent detection of a data signal and said second signal component is said data signal.
18. The method of claim 1, further comprising the step of:
c) the value of N is adaptively changed.
19. The method of claim 18, wherein said step c) adaptively changes said value of N based on linear coefficients of at least one of said N blocks.
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| US09/296,654 US6542560B1 (en) | 1999-04-23 | 1999-04-23 | Method of channel estimation and compensation based thereon |
| US09/296,654 | 1999-04-23 |
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| EP (1) | EP1047232A3 (en) |
| JP (1) | JP3993734B2 (en) |
| KR (1) | KR100662223B1 (en) |
| CN (1) | CN1272003A (en) |
| AU (1) | AU2886100A (en) |
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- 1999-04-23 US US09/296,654 patent/US6542560B1/en not_active Expired - Lifetime
-
2000
- 2000-04-10 BR BR0004837-2A patent/BR0004837A/en not_active Application Discontinuation
- 2000-04-18 AU AU28861/00A patent/AU2886100A/en not_active Abandoned
- 2000-04-18 EP EP00303271A patent/EP1047232A3/en not_active Withdrawn
- 2000-04-18 CA CA002305987A patent/CA2305987A1/en not_active Abandoned
- 2000-04-21 CN CN00106918A patent/CN1272003A/en active Pending
- 2000-04-21 KR KR1020000021207A patent/KR100662223B1/en not_active Expired - Fee Related
- 2000-04-24 JP JP2000123004A patent/JP3993734B2/en not_active Expired - Fee Related
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101233560B (en) * | 2005-06-17 | 2011-08-03 | 林翰 | Method and device for recovering audio signal |
| CN108958092A (en) * | 2017-05-23 | 2018-12-07 | 青岛海尔洗衣机有限公司 | Clock method for detecting abnormality and device, computer readable storage medium, equipment |
| CN108958092B (en) * | 2017-05-23 | 2022-11-04 | 佛山市顺德海尔电器有限公司 | Method and device for detecting abnormality of single-chip clock clock, computer-readable storage medium, and device |
Also Published As
| Publication number | Publication date |
|---|---|
| EP1047232A3 (en) | 2002-01-02 |
| KR100662223B1 (en) | 2007-01-02 |
| CA2305987A1 (en) | 2000-10-23 |
| BR0004837A (en) | 2001-01-23 |
| KR20000071763A (en) | 2000-11-25 |
| EP1047232A2 (en) | 2000-10-25 |
| JP2000353993A (en) | 2000-12-19 |
| US6542560B1 (en) | 2003-04-01 |
| JP3993734B2 (en) | 2007-10-17 |
| AU2886100A (en) | 2000-10-26 |
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